skip to main content
10.1145/3660043.3660112acmotherconferencesArticle/Chapter ViewAbstractPublication PagesicieaiConference Proceedingsconference-collections
research-article

Cross-domain Pedestrian Re-recognition Research by Fusing Pedestrian Detection Algorithms

Published: 30 May 2024 Publication History

Abstract

Pedestrian re-identification is a computer vision technique designed to identify the same pedestrian under different cameras, which is of great significance in fields such as public safety and intelligent transportation. In this paper, a pedestrian re-recognition model incorporating YOLOv7 target detection and pedestrian re-recognition algorithm is proposed. Firstly, a small target detection layer is added to the YOLOv7 detection algorithm, and a coordinate attention mechanism is added to the backbone network to improve the pedestrian detection capability. Cross-camera retrieval suffers from cross-domain problem, in this paper, by adding IBN-b module to the pedestrian re-recognition network, adding label smoothing strategy and removing random erasure during training, and using multiple datasets for co-training, and then verifying on Duke dataset, Rank-1 reaches 70.8% and mAP reaches 54.8%, which can meet the requirements of real scene applications.

References

[1]
X. L. Qian, Y. W. Fu, Y. G. Jiang, Multi-scale Deep Learning Architectures for Person Re-identification[C]. IEEE International Conference on Computer Vision. 2017: 5409-5418.
[2]
Y. F. Sun, L. Zheng, Y. Yang, Beyond Part Models: Person Retrieval with Refined Part Pooling (and A Strong Convolutional Baseline)[C]. Lecture Notes in Computer Science. 2018: 501-518.
[3]
X. L. Qian, Y. W. Fu, T. Xiang, Pose-Normalized Image Generation for Person Re-identification[C]. Lecture Notes in Computer Science. 2018: 661-678.
[4]
C. Liu, X. J. Chang, Y. D. Shen, Unity Style Transfer for Person Re-Identification[C]. IEEE Conference on Computer Vision and Pattern Recognition. 2020: 6886-6895.
[5]
R. Girshick, J. Donahue, T. Darrell, Rich feature hierarchies for accurate object detection and semantic segmentation[C]. IEEE Conference on Computer Vision and Pattern Recognition. 2014: 580-587.
[6]
J. Redmon, S. Divvala, R. Girshick, You Only Look Once: Unified, Real-Time Object Detection[C]. IEEE Conference on Computer Vision and Pattern Recognition. 2016: 779-788.
[7]
Chien-Yao Wang, Alexey Bochkovskiy, Hong-Yuan Mark Liao. YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors[J]. Arxiv, 2022,
[8]
J. Hu, L. Shen, S. Albanie, Squeeze-and-Excitation Networks[J]. Ieee Transactions on Pattern Analysis and Machine Intelligence, 2020, 42(8): 2011-2023.
[9]
Jongchan Park, Sanghyun Woo, Joon-Young Lee, BAM: Bottleneck Attention Module[J]. Arxiv, 2018,
[10]
Q. B. Hou, D. Q. Zhou, J. S. Feng, Coordinate Attention for Efficient Mobile Network Design[C]. IEEE Conference on Computer Vision and Pattern Recognition. 2021: 13708-13717.
[11]
K. M. He, X. Y. Zhang, S. Q. Ren, Deep Residual Learning for Image Recognition[C]. IEEE Conference on Computer Vision and Pattern Recognition. 2016: 770-778.
[12]
Z. Zhong, L. Zheng, G. L. Kang, Random Erasing Data Augmentation[C]. AAAI Conference on Artificial Intelligence. 2020: 13001-13008.
[13]
X. G. Pan, P. Luo, J. P. Shi, Two at Once: Enhancing Learning and Generalization Capacities via IBN-Net[C]. Lecture Notes in Computer Science. 2018: 484-500.
[14]
C. Szegedy, V. Vanhoucke, S. Ioffe, Rethinking the Inception Architecture for Computer Vision[C]. IEEE Conference on Computer Vision and Pattern Recognition. 2016: 2818-2826.

Recommendations

Comments

Information & Contributors

Information

Published In

cover image ACM Other conferences
ICIEAI '23: Proceedings of the 2023 International Conference on Information Education and Artificial Intelligence
December 2023
1132 pages
ISBN:9798400716157
DOI:10.1145/3660043
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 30 May 2024

Permissions

Request permissions for this article.

Check for updates

Qualifiers

  • Research-article
  • Research
  • Refereed limited

Conference

ICIEAI 2023

Contributors

Other Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

  • 0
    Total Citations
  • 8
    Total Downloads
  • Downloads (Last 12 months)8
  • Downloads (Last 6 weeks)3
Reflects downloads up to 05 Mar 2025

Other Metrics

Citations

View Options

Login options

View options

PDF

View or Download as a PDF file.

PDF

eReader

View online with eReader.

eReader

HTML Format

View this article in HTML Format.

HTML Format

Figures

Tables

Media

Share

Share

Share this Publication link

Share on social media